Education, Governance, Trade and Distance: Impact on Technology Diffusion and the East Asia-Latin America Productivity Gap
Bibliographic record
Abstract
This paper examines the impact of education, trade, governance and distance on technology diffusion and TFP in Latin America – specifically South America and Mexico (SAM) – and East Asia, over the 32 years preceding the Great Recession (1976–2007). Findings are: i) TFP rises with education, trade, governance (ETG) and trade's R&D content, and falls with distance to the (closest) North; ii) the East Asia – SAM education gap's impact equals that of trade plus governance; iii) an increase in SAM's ETG to East Asia's level raises TFP by over 100 percent and fully accounts for its TFP gap with East Asia; and iv) South America's TFP loss relative to Mexico due to its greater distance to 'US–Canada' (Europe and Japan) is 9.30 (0.02) percent.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".